Data analysis of sunspot time series with SSA and HHT information adaptive methods

Nikolai T. Safiullin, Sergey V. Porshnev, Nathan I. Kleeorin

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


In the paper, characteristics of the monthly Wolf numbers time series data, containing information about individual solar cycles, are investigated by decomposition into components with two time series methods, namely, the Singular Spectrum Analysis (SSA) and Huang- Hilbert Transform (HHT). These methods do not require any a priori knowledge about analyzed data, making them information adaptive. As a result, some of the known cycles such as Schwabe-Wolf Cycle, Hale Cycle, Gleisberg Cycle, and Suess Cycle have been identified. These components and their properties are compared with each other, as well as with known characteristics of sunspot cycles.

Original languageEnglish
Pages (from-to)110-119
Number of pages10
JournalCEUR Workshop Proceedings
StatePublished - 1 Jan 2017
Event2nd International Workshop on Radio Electronics and Information Technologies, REIT 2 2017 - Yekaterinburg, Russian Federation
Duration: 15 Nov 2017 → …


  • Data analysis
  • Empirical mode decomposition
  • Information handling
  • Singular spectrum analysis
  • Sunspot numbers
  • Time series

ASJC Scopus subject areas

  • Computer Science (all)


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